title: Comprehensive cluster analysis with Transitivity Clustering.
abstract: Transitivity Clustering is a method for the partitioning of biological data into groups of similar objects, such as genes, for instance. It provides integrated access to various functions addressing each step of a typical cluster analysis. To facilitate this, Transitivity Clustering is accessible online and offers three user-friendly interfaces: a powerful stand-alone version, a web interface, and a collection of Cytoscape plug-ins. In this paper, we describe three major workflows: (i) protein (super)family detection with Cytoscape, (ii) protein homology detection with incomplete gold standards and (iii) clustering of gene expression data. This protocol guides the user through the most important features of Transitivity Clustering and takes ∼1 h to complete.
authors.affiliation: Buck Institute for Age Research, Novato, California, USA.
authors.forename: Tobias
authors.initials: T
authors.lastname: Wittkop
authors.forename: Dorothea
authors.initials: D
authors.lastname: Emig
authors.forename: Anke
authors.initials: A
authors.lastname: Truss
authors.forename: Mario
authors.initials: M
authors.lastname: Albrecht
authors.forename: Sebastian
authors.initials: S
authors.lastname: Böcker
authors.forename: Jan
authors.initials: J
authors.lastname: Baumbach
mesh_terms: D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D030561:Databases, Nucleic Acid; D030562:Databases, Protein; D020869:Gene Expression Profiling; D020407:Internet; D008969:Molecular Sequence Data; D010363:Pattern Recognition, Automated / Q000379:methods*; D016415:Sequence Alignment / Q000379:methods*; D017421:Sequence Analysis / Q000379:methods; D017385:Sequence Homology; D012984:Software; D014584:User-Computer Interface